Analytics of Public Reactions to the COVID-19 Vaccine on Twitter Using Sentiment Analysis and Topic Modelling

Abstract

The number of new COVID-19 infections and deaths is still increasing worldwide, which led governments to take a series of mandatory actions. The COVID-19 vaccine announcement kindled the various rays of emotions among the social media users. Thus, this chapter aims to discover public reaction regarding the COVID-19 vaccine posts on a social media platform, specifically Twitter, to extract the most discussed topics during the period April 25, 2021 to May 2, 2021. The extraction was based on a dataset of English tweets pertinent to the COVID-19 vaccine. The Latent Dirichlet Allocation (LDA) was adopted for topics extraction whereas VADER lexicon-based approach was applied for sentiment analysis. Based on the results, most tweets expressed neutral and positive opinions about the COVID-19 vaccine. Regarding the latent themes discovered about the vaccine, most of topics have exposed the public trust towards the COVID-19 vaccine compared with the mistrust ones. This study can assist governments and policy makers to track public opinions for better decision-making during pandemics.